A novel updating strategy for associative memory scheme in cyclic dynamic environments

Abstract

Associative memory schemes have been developed for Evolutionary Algorithms (EAs) to solve Dynamic Optimization Problems (DOPs), and demonstrated powerful performance. In these schemes, how to update the memory could be important for their performance. However, little work has been done about the associative memory updating strategies. In this paper, a novel updating strategy is proposed for associative memory schemes. In this strategy, the memory point whose associated environmental information is most similar to the current environmental information is first picked out from the memory. Then, the selected memory individual is updated according to the fitness value, and the associated environmental information is updated according to the matching degree between environmental information and individuals. This updating strategy is embedded into a state-of-the-art algorithm, i.e. the MPBIL, and tested by experiments. Experimental results demonstrate that the proposed updating strategy is helpful for associative memory schemes to enhance their search ability in cyclic dynamic environments.